Named entity recognition visual context and caption data

ABSTRACT

A caption of a multimodal message (e.g., social media post) can be identified as a named entity using an entity recognition system. The entity recognition system can use a visual attention based mechanism to generate a visual context representation from an image and caption. The system can use the visual context representation to identify one or more terms of the caption as a named entity.

CLAIM FOR PRIORITY

This application is a continuation of U.S. patent application Ser. No.17/306,010, filed on May 3, 2021, which is a continuation of U.S. patentapplication Ser. No. 16/230,341, filed on Dec. 21, 2018, which claimsthe benefit of priority to U.S. Application Ser. No. 62/610,051, filedDec. 22, 2017, each of which is hereby incorporated by reference in itsentirety.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to machinelearning and, more particularly, but not by way of limitation, toidentifying named entities using machine learning.

BACKGROUND

Named Entity Recognition (NER) is a computational task in which one ormore words of text are determined to be a named entity (e.g., a noun,celebrity, city, organization). For example, an NER scheme may assume apop song exists called “Modern Baseball”. Given a sentence “I lovemodern baseball!” as an input, a NER model attempts to determine whetherone or more terms of the sentence are a named entity, e.g., whether thesentence is expression of love for the sport baseball in modern times ora pop song with a title of “Modern Baseball”. Typically, an NER schemeneeds a large well-structured dataset from which it can train a model,which can then be applied to recognize one or more words as a namedentity. However, recognizing a named entity from a few words, some ofwhich may be intentionally misspelled (e.g., “I luv new yoooooorkctiy!”) is difficult for current NER schemes.

BRIEF DESCRIPTION OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure (“FIG.”) number in which that element or act is first introduced.

FIG. 1 is a block diagram showing an example messaging system forexchanging data (e.g., messages and associated content) over a network.

FIG. 2 is block diagram illustrating further details regarding amessaging system having an integrated virtual object machine learningsystem, according to example embodiments.

FIG. 3 is a schematic diagram illustrating data that may be stored in adatabase of a messaging server system, according to certain exampleembodiments.

FIG. 4 is a schematic diagram illustrating a structure of a message,according to some embodiments, generated by a messaging clientapplication for communication.

FIG. 5 is a schematic diagram illustrating an example access-limitingprocess, in terms of which access to content (e.g., an ephemeralmessage, and associated multimedia payload of data) or a contentcollection (e.g., an ephemeral message story) may be time-limited (e.g.,made ephemeral).

FIG. 6 shows internal functional components of a visual gazetteer namedentity system, according to some example embodiments.

FIG. 7A shows a flow diagram of a method for identifying named entitiesin a caption of a multimodal message using visual attention based imagedata and gazetteer data, according to some example embodiments.

FIG. 7B shows a flow diagram of method of identifying named entitiesusing a visual attention mechanism, according to some exampleembodiments.

FIG. 8 shows an architecture for generating named entity recognitiontags using visual attention word-based data and gazetteer vectors,according to some example embodiments.

FIG. 9 shows an example architecture of the gazetteer engine, accordingto some example embodiments.

FIG. 10 shows an architecture for a lexical generator, according to someexample embodiments.

FIG. 11 shows an example architecture of the classification engine,according to some example embodiments.

FIG. 12 shows an architecture for the visual word engine and theclassification engine, according to some example embodiments.

FIG. 13A shows an image of the multimodal message, according to someexample embodiments.

FIG. 13B shows an example image that corresponds to the image withcertain areas being highlighted according to the caption.

FIG. 14A shows an example multimodal message, according to some exampleembodiments.

FIG. 14B shows example overlay content that has been identified usingnamed entity recognition, according to some example embodiments.

FIG. 15 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 16 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

As discussed, named entity recognition is a difficult computationaltask. Further, performing NER using social media posts is made moredifficult due to the shortness of the captions, intentional misspellingof words, acronyms, and emojis being used in a certain way based onsocietal trends. To this end, a visual named entity system can beimplemented to identify named entities in multimodal captions using avisual attention network and vector information from words andcharacters in a caption. In some example embodiments, an image of amultimodal message is processed via an attention network to indicatewhich portions of the region are more relevant to a caption thataccompanies the image. The attention network can generate a visualcontext vector from the image and the caption which can be integratedinto a recurrent neural network (e.g., a bidirectional long short termmemory (LSTM) having conditional random field layer), which can indicatewhich terms in the caption correspond to a named entity. The identifiednamed entity can be used to select and incorporate content for inclusionin a social media post. For example, the visual named entity system candetermine that a certain band is being discussed in a caption andsuggest a guitar emoji for inclusion or overlay in a social media post.

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple client devices 102, each ofwhich hosts a number of applications including a messaging clientapplication 104. Each messaging client application 104 iscommunicatively coupled to other instances of the messaging clientapplication 104 and a messaging server system 108 via a network 106(e.g., the Internet).

Accordingly, each messaging client application 104 is able tocommunicate and exchange data with another messaging client application104 and with the messaging server system 108 via the network 106. Thedata exchanged between messaging client applications 104, and between amessaging client application 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video, or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described herein asbeing performed by either a messaging client application 104 or by themessaging server system 108, it will be appreciated that the location ofcertain functionality within either the messaging client application 104or the messaging server system 108 is a design choice. For example, itmay be technically preferable to initially deploy certain technology andfunctionality within the messaging server system 108, and to latermigrate this technology and functionality to the messaging clientapplication 104 where a client device 102 has a sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Thisdata may include message content, client device information, geolocationinformation, media annotation and overlays, message content persistenceconditions, social network information, and live event information, asexamples. Data exchanges within the messaging system 100 are invoked andcontrolled through functions available via user interfaces (UIs) of themessaging client application 104.

Turning now specifically to the messaging server system 108, anapplication programming interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112.

The API server 110 receives and transmits message data (e.g., commandsand message payloads) between the client devices 102 and the applicationserver 112. Specifically, the API server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client application 104 in order to invoke functionalityof the application server 112. The API server 110 exposes variousfunctions supported by the application server 112, including accountregistration; login functionality; the sending of messages, via theapplication server 112, from a particular messaging client application104 to another messaging client application 104; the sending of mediafiles (e.g., images or video) from a messaging client application 104 toa messaging server application 114 for possible access by anothermessaging client application 104; the setting of a collection of mediadata (e.g., a story); the retrieval of such collections; the retrievalof a list of friends of a user of a client device 102; the retrieval ofmessages and content; the adding and deletion of friends to and from asocial graph; the location of friends within the social graph; andopening application events (e.g., relating to the messaging clientapplication 104).

The application server 112 hosts a number of applications andsubsystems, including the messaging server application 114, an imageprocessing system 116, and a social network system 122. The messagingserver application 114 implements a number of message-processingtechnologies and functions particularly related to the aggregation andother processing of content (e.g., textual and multimedia content)included in messages received from multiple instances of the messagingclient application 104. As will be described in further detail, the textand media content from multiple sources may be aggregated intocollections of content (e.g., called stories or galleries). Thesecollections are then made available, by the messaging server application114, to the messaging client application 104. Other processor- andmemory-intensive processing of data may also be performed server-side bythe messaging server application 114, in view of the hardwarerequirements for such processing.

The application server 112 also includes the image processing system116, which is dedicated to performing various image processingoperations, typically with respect to images or video received withinthe payload of a message at the messaging server application 114.

The social network system 122 supports various social networkingfunctions and services, and makes these functions and services availableto the messaging server application 114. To this end, the social networksystem 122 maintains and accesses an entity graph (e.g., entity graph304 in FIG. 3 ) within the database 120. Examples of functions andservices supported by the social network system 122 include theidentification of other users of the messaging system 100 with whom aparticular user has relationships or whom the particular user is“following,” and also the identification of other entities and interestsof a particular user.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data associated with messages processed by the messaging serverapplication 114.

FIG. 2 is block diagram illustrating further details regarding themessaging system 100, according to example embodiments. Specifically,the messaging system 100 is shown to comprise the messaging clientapplication 104 and the application server 112, which in turn embody anumber of subsystems, namely an ephemeral timer system 202, a collectionmanagement system 204, an annotation system 206.

The ephemeral timer system 202 is responsible for enforcing thetemporary access to content permitted by the messaging clientapplication 104 and the messaging server application 114. To this end,the ephemeral timer system 202 incorporates a number of timers that,based on duration and display parameters associated with a message orcollection of messages (e.g., an Ephemeral Message Story), selectivelydisplay and enable access to messages and associated content via themessaging client application 104. Further details regarding theoperation of the ephemeral timer system 202 are provided below.

The collection management system 204 is responsible for managingcollections of media (e.g., collections of text, image, video, and audiodata). In some examples, a collection of content (e.g., messages,including images, video, text, and audio) may be organized into an“event gallery” or an “event story.” Such a collection may be madeavailable for a specified time period, such as the duration of an eventto which the content relates. For example, content relating to a musicconcert may be made available as a “story” for the duration of thatmusic concert. The collection management system 204 may also beresponsible for publishing an icon that provides notification of theexistence of a particular collection to the user interface of themessaging client application 104.

The collection management system 204 furthermore includes a curationinterface 208 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface208 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certainembodiments, compensation may be paid to a user for inclusion ofuser-generated content into a collection. In such cases, the curationinterface 208 operates to automatically make payments to such users forthe use of their content.

The annotation system 206 provides various functions that enable a userto annotate or otherwise modify or edit media content associated with amessage. For example, the annotation system 206 provides functionsrelated to the generation and publishing of media overlays for messagesprocessed by the messaging system 100. The annotation system 206operatively supplies a media overlay (e.g., a geofilter or filter) tothe messaging client application 104 based on a geolocation of theclient device 102. In another example, the annotation system 206operatively supplies a media overlay to the messaging client application104 based on other information, such as social network information ofthe user of the client device 102. A media overlay may include audio andvisual content and visual effects. Examples of audio and visual contentinclude pictures, text, logos, animations, and sound effects. An exampleof a visual effect includes color overlaying. The audio and visualcontent or the visual effects can be applied to a media content item(e.g., a photo) at the client device 102. For example, the media overlayincludes text that can be overlaid on top of a photograph generated bythe client device 102. In another example, the media overlay includes anidentification of a location (e.g., Venice Beach), a name of a liveevent, or a name of a merchant (e.g., Beach Coffee House). In anotherexample, the annotation system 206 uses the geolocation of the clientdevice 102 to identify a media overlay that includes the name of amerchant at the geolocation of the client device 102. The media overlaymay include other indicia associated with the merchant. The mediaoverlays may be stored in the database 120 and accessed through thedatabase server 118.

In one example embodiment, the annotation system 206 provides auser-based publication platform that enables users to select ageolocation on a map and upload content associated with the selectedgeolocation. The user may also specify circumstances under whichparticular content should be offered to other users. The annotationsystem 206 generates a media overlay that includes the uploaded contentand associates the uploaded content with the selected geolocation.

In another example embodiment, the annotation system 206 provides amerchant-based publication platform that enables merchants to select aparticular media overlay associated with a geolocation via a biddingprocess. For example, the annotation system 206 associates the mediaoverlay of a highest-bidding merchant with a corresponding geolocationfor a predefined amount of time.

The visual named entity recognition system 210 comprises one or moreneural networks configured to identify an entity referenced by amultimodal message, as discussed in further detail below. In someexample embodiments, the visual named entity recognition system 210 isintegrated and run from the application server 112.

FIG. 3 is a schematic diagram illustrating data 300 which may be storedin the database 120 of the messaging server system 108, according tocertain example embodiments. While the content of the database 120 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 120 includes message data stored within a message table314. An entity table 302 stores entity data, including an entity graph304. Entities for which records are maintained within the entity table302 may include individuals, corporate entities, organizations, objects,places, events, and so forth. Regardless of type, any entity regardingwhich the messaging server system 108 stores data may be a recognizedentity. Each entity is provided with a unique identifier, as well as anentity type identifier (not shown).

The entity graph 304 furthermore stores information regardingrelationships and associations between or among entities. Suchrelationships may be social, professional (e.g., work at a commoncorporation or organization), interest-based, or activity-based, forexample.

The database 120 also stores annotation data, in the example form offilters, in an annotation table 312. Filters for which data is storedwithin the annotation table 312 are associated with and applied tovideos (for which data is stored in a video table 310) and/or images(for which data is stored in an image table 308). Filters, in oneexample, are overlays that are displayed as overlaid on an image orvideo during presentation to a recipient user. Filters may be of varioustypes, including user-selected filters from a gallery of filterspresented to a sending user by the messaging client application 104 whenthe sending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client application 104, basedon geolocation information determined by a Global Positioning System(GPS) unit of the client device 102. Another type of filter is a datafilter, which may be selectively presented to a sending user by themessaging client application 104, based on other inputs or informationgathered by the client device 102 during the message creation process.Examples of data filters include a current temperature at a specificlocation, a current speed at which a sending user is traveling, abattery life for a client device 102, or the current time.

Other annotation data that may be stored within the image table 308 isso-called “lens” data. A “lens” may be a real-time special effect andsound that may be added to an image or a video.

As mentioned above, the video table 310 stores video data which, in oneembodiment, is associated with messages for which records are maintainedwithin the message table 314. Similarly, the image table 308 storesimage data associated with messages for which message data is stored inthe message table 314. The entity table 302 may associate variousannotations from the annotation table 312 with various images and videosstored in the image table 308 and the video table 310.

A story table 306 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhom a record is maintained in the entity table 302). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client application 104 may include an icon that isuser-selectable to enable a sending user to add specific content to hisor her personal story.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices 102 havelocation services enabled and are at a common location or event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client application 104, to contributecontent to a particular live story. The live story may be identified tothe user by the messaging client application 104 based on his or herlocation. The end result is a “live story” told from a communityperspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some embodiments, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some embodiments, generated by a messaging clientapplication 104 for communication to a further messaging clientapplication 104 or the messaging server application 114. The content ofa particular message 400 is used to populate the message table 314stored within the database 120, accessible by the messaging serverapplication 114. Similarly, the content of a message 400 is stored inmemory as “in-transit” or “in-flight” data of the client device 102 orthe application server 112. The message 400 is shown to include thefollowing components:

-   -   A message identifier 402: a unique identifier that identifies        the message 400.    -   A message text payload 404: text, to be generated by a user via        a user interface of the client device 102, and that is included        in the message 400.    -   A message image payload 406: image data captured by a camera        component of a client device 102 or retrieved from memory of a        client device 102, and that is included in the message 400.    -   A message video payload 408: video data captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400.    -   A message audio payload 410: audio data captured by a microphone        or retrieved from the memory component of the client device 102,        and that is included in the message 400.    -   Message annotations 412: annotation data (e.g., filters,        stickers, or other enhancements) that represents annotations to        be applied to the message image payload 406, message video        payload 408, or message audio payload 410 of the message 400.    -   A message duration parameter 414: a parameter value indicating,        in seconds, the amount of time for which content of the message        400 (e.g., the message image payload 406, message video payload        408, and message audio payload 410) is to be presented or made        accessible to a user via the messaging client application 104.    -   A message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message 400. Multiple message geolocation        parameter 416 values may be included in the payload, with each        of these parameter values being associated with respective        content items included in the content (e.g., a specific image in        the message image payload 406, or a specific video in the        message video payload 408).    -   A message story identifier 418: identifies values identifying        one or more content collections (e.g., “stories”) with which a        particular content item in the message image payload 406 of the        message 400 is associated. For example, multiple images within        the message image payload 406 may each be associated with        multiple content collections using identifier values.    -   A message tag 420: one or more tags, each of which is indicative        of the subject matter of content included in the message        payload. For example, where a particular image included in the        message image payload 406 depicts an animal (e.g., a lion), a        tag value may be included within the message tag 420 that is        indicative of the relevant animal. Tag values may be generated        manually, based on user input, or may be automatically generated        using, for example, image recognition.    -   A message sender identifier 422: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 on        which the message 400 was generated and from which the message        400 was sent.    -   A message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of the message 400may be pointers to locations in tables within which content data valuesare stored. For example, an image value in the message image payload 406may be a pointer to (or address of) a location within the image table308. Similarly, values within the message video payload 408 may point todata stored within the video table 310, values stored within the messageannotations 412 may point to data stored in the annotation table 312,values stored within the message story identifier 418 may point to datastored in the story table 306, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within the entity table 302.

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to content (e.g., an ephemeral message502, and associated multimedia payload of data) or a content collection(e.g., an ephemeral message story 504) may be time-limited (e.g., madeephemeral).

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client application 104. Inone embodiment, where the messaging client application 104 is anapplication client, an ephemeral message 502 is viewable by a receivinguser for up to a maximum of 10 seconds, depending on the amount of timethat the sending user specifies using the message duration parameter506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 512, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 512 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message story 504 (e.g., a personal story, or an event story).The ephemeral message story 504 has an associated story durationparameter 508, a value of which determines a time duration for which theephemeral message story 504 is presented and accessible to users of themessaging system 100. The story duration parameter 508, for example, maybe the duration of a music concert, where the ephemeral message story504 is a collection of content pertaining to that concert.Alternatively, a user (either the owning user or a curator user) mayspecify the value for the story duration parameter 508 when performingthe setup and creation of the ephemeral message story 504.

Additionally, each ephemeral message 502 within the ephemeral messagestory 504 has an associated story participation parameter 510, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message story504. Accordingly, a particular ephemeral message 502 may “expire” andbecome inaccessible within the context of the ephemeral message story504, prior to the ephemeral message story 504 itself expiring in termsof the story duration parameter 508. The story duration parameter 508,story participation parameter 510, and message receiver identifier 424each provide input to a story timer 514, which operationally determineswhether a particular ephemeral message 502 of the ephemeral messagestory 504 will be displayed to a particular receiving user and, if so,for how long. Note that the ephemeral message story 504 is also aware ofthe identity of the particular receiving user as a result of the messagereceiver identifier 424.

Accordingly, the story timer 514 operationally controls the overalllifespan of an associated ephemeral message story 504, as well as anindividual ephemeral message 502 included in the ephemeral message story504. In one embodiment, each and every ephemeral message 502 within theephemeral message story 504 remains viewable and accessible for a timeperiod specified by the story duration parameter 508. In a furtherembodiment, a certain ephemeral message 502 may expire, within thecontext of the ephemeral message story 504, based on a storyparticipation parameter 510. Note that a message duration parameter 506may still determine the duration of time for which a particularephemeral message 502 is displayed to a receiving user, even within thecontext of the ephemeral message story 504. Accordingly, the messageduration parameter 506 determines the duration of time that a particularephemeral message 502 is displayed to a receiving user, regardless ofwhether the receiving user is viewing that ephemeral message 502 insideor outside the context of an ephemeral message story 504.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message story 504based on a determination that it has exceeded an associated storyparticipation parameter 510. For example, when a sending user hasestablished a story participation parameter 510 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message story 504 after thespecified 24 hours. The ephemeral timer system 202 also operates toremove an ephemeral message story 504 either when the storyparticipation parameter 510 for each and every ephemeral message 502within the ephemeral message story 504 has expired, or when theephemeral message story 504 itself has expired in terms of the storyduration parameter 508.

In certain use cases, a creator of a particular ephemeral message story504 may specify an indefinite story duration parameter 508. In thiscase, the expiration of the story participation parameter 510 for thelast remaining ephemeral message 502 within the ephemeral message story504 will determine when the ephemeral message story 504 itself expires.In this case, a new ephemeral message 502, added to the ephemeralmessage story 504, with a new story participation parameter 510,effectively extends the life of an ephemeral message story 504 to equalthe value of the story participation parameter 510.

In response to the ephemeral timer system 202 determining that anephemeral message story 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (e.g., specifically, the messaging client application 104) to causean indicium (e.g., an icon) associated with the relevant ephemeralmessage story 504 to no longer be displayed within a user interface ofthe messaging client application 104. Similarly, when the ephemeraltimer system 202 determines that the message duration parameter 506 fora particular ephemeral message 502 has expired, the ephemeral timersystem 202 causes the messaging client application 104 to no longerdisplay an indicium (e.g., an icon or textual identification) associatedwith the ephemeral message 502.

FIG. 6 shows internal functional components of a visual named entitysystem 210, according to some example embodiments. As illustrated, thevisual named entity recognition system 210 comprises an interface engine605, a word engine 610, a part-of-speech engine 615, a character engine620, a gazetteer engine 625, a visual word engine 630, a combinationengine 635, a classification engine 640, and a message engine 645. Theinterface engine 605 manages identification or otherwise generation ofone or more images using an image capture device (e.g. a camera) of theclient device. The interface engine 605 may also identify or generate amultimodal message that has one or more images (e.g. an image, a videosequence), audio data captured simultaneously with the one or moreimages by the client device, and a caption input by the user of theclient device. The word engine 610 is a neural network configured togenerate word embeddings, according to some example embodiments. Thepart-of-speech engine 615 is configured to receive one or more words(e.g. a sentence) and label each of the words using a part of speechtag. For example, the part-of-speech engine 615 may label one word as anoun, another word is a preposition, and yet another word as a verb, andso on. The character engine 620 is a neural network configured togenerate character embeddings from one or more words (e.g. words in thecaption of the multimodal message identified or otherwise generated bythe interface engine 605). The gazetteer engine 625 is configured togenerate gazetteer vectors for each of the words in the caption of themultimodal message. A gazetteer is a list of words of a particular type,such as a list of celebrities, a list of places, a list oforganizations, a list of sports teams, and so on. In some exampleembodiments, one or more words of the caption in the multimodal messageare identified as corresponding to a gazetteer vector and the message istagged with the gazetteer tag. Each gazetteer tag has a correspondinggazetteer vector in a gazetteer tag lookup table. In some exampleembodiments, the gazetteer tag lookup table comprises eight vectorswhich corresponds to eight gazetteer's. In some example embodiments,each word can be used to generate up to eight gazetteer vectors from thelookup table. For the given word, each of the vectors can beconcatenated to represent that word's final gazetteer feature vector.

The visual word engine 630 is configured to generate a word vector froman image and caption of the multimodal message. In particular, and asexplained in further detail below, the image can be processed using aconvolution on neural network and the caption can be processed using abidirectional recurrent neural network. The bidirectional recurrentneural network can generate a vector that can be used to call attentionto or otherwise emphasize certain regions of the image that morestrongly correspond to nouns and verbs in the caption. The combinationengine 635 is configured to generate a combined vector that, for a givenword in the caption, concatenates the part-of-speech representation forthat word, the word embedding for that word, the gazetteerrepresentation for that word, a forward time LSTM character embedding,and backward time LSTM character embedding.

The classification engine 640 is configured to receive the combinedvector generated by the combination engine 635 and the visual wordvector generated by the visual word engine 630, and recursively generatean indication whether the words in the caption correspond to a namedentity. The message engine 645 is configured to select overlay contentthat has been pre-associated with an identified named entity in thecaption, and overlay the overlay content on the multimodal message. Themessage engine 645 can also manage publishing the multimodal messagewith the overlay content as an ephemeral message on a social networksite.

FIG. 7A shows a flow diagram of a method 700 for identifying namedentities in a caption of a multimodal message using visual attentionbased image data and gazetteer data, according to some exampleembodiments. At operation 705, the interface engine 605 generates themultimodal message. For example, the interface engine 605 may use acamera of the client device 102 to capture video, and an audio sensor ofthe client device 102 to simultaneously capture voice data and mayfurther receive a caption input by the user of the client device 102 forinclusion with the audio data and the video data in the multimodalmessage.

At operation 710, the gazetteer engine 625 analyzes each word in thecaption of the multimodal message and generates a gazetteer vector forthe words in the caption that correspond to gazetteer's (e.g. lists ofcelebrities, lists of cities, and so on). At operation 715, thecharacter engine 620 generates character vectors from each character inthe caption of the multimodal message. At operation 720, the visual wordengine 630 generates a visual word vector from the image of themultimodal message and the caption of the multimodal message, asdiscussed in further detail below with reference to FIG. 12 .

At operation 725, the classification engine 640 labels one or more wordsin the caption of the multimodal message as a named entity. At operation730, the message engine 645 identifies content pre-associated with theidentified entity. At operation 735, the message engine 645 generates anephemeral message from the associated content and the multimodalmessage. For example, the message engine 645 may overlay thepre-associated content on the multimodal message to generate theephemeral message. At operation 740, the message engine 645 publishesthe ephemeral message on a network site, such as a social media websiteor a network service that provides the ephemeral message is to one ormore client devices via the messaging client application 104.

FIG. 7B shows a flow diagram of a method 750 for identifying a namedentity without gazetteer, according to some example embodiments. In theexample of FIG. 7B, entities are named using the visual word engine 630(e.g., architecture 1200 of FIG. 12 ) without the user of lists orgazetteers. At operation 755, the interface engine 605 generates themultimodal message. For example, the interface engine 605 may use acamera of the client device 102 to capture video, and an audio sensor ofthe client device 102 to simultaneously capture voice data and mayfurther receive a caption input by the user of the client device 102 forinclusion with the audio data and the video data in the multimodalmessage.

At operation 760, the visual word engine 630 generates a visual contextvector using the caption and the image. For example, the caption isencoded as a query using a LSTM, and visual representations (e.g.,global and/or regional representation) are generated using aconvolutional neural network. The query and the visual representationsare input into an attention neural network which then generates thevisual context vector (e.g., as discussed in further detail below withreference to FIG. 12 ).

At operation 760, the visual word engine 630 integrates the visualcontext vector into an entity recognition neural network. The entityrecognition neural network comprises a bidirectional LSTM layer, aconditional random field layer, and, optionally, a modulated gate layer,according to some example embodiments. In some example embodiments, thevisual context vector is used to seed or initialize a forward LSTM cellof the entity recognition neural network. In some example embodiments,the visual context vector is integrated at the word-level (e.g., foreach word of the caption) using the modulated gate layer, as discussedin further detail below, with reference to FIG. 12 .

At operation 770, the classification engine 640 labels one or more wordsin the caption of the multimodal message as a named entity. At operation775, the message engine 645 identifies content pre-associated with theidentified entity. As used here, pre-associated means after the neuralnetwork is trained but before the network model is distributed to clientdevices; that is, pre-associated means before run time by an end user ofclient device 102. At operation 780, the message engine 645 generates anephemeral message from the associated content and the multimodalmessage. For example, the message engine 645 may overlay thepre-associated content on the multimodal message to generate theephemeral message. At operation 785, the message engine 645 publishesthe ephemeral message on a network site, such as a social media websiteor a network service that provides the ephemeral message is to one ormore client devices via the messaging client application 104.

Although the operations of method 750 are discussed as separate steps,it is appreciated by those having ordinary skill in the art of neuralnetworks that some of the operations (e.g., operations 760, 765, 770)are performed as one step by a neural network architecture (e.g.,architecture 1200) which is trained via end-to-end training.

FIG. 8 shows an architecture 800 for generating named entity recognitiontags using visual attention word-based data and gazetteer vectors,according to some example embodiments. As illustrated, a multimodalmessage 805 is input into a lexical generator 810. The lexicalgenerator, can comprise the word engine 610, the part-of-speech engine615, the gazetteer engine 625, the character engine 620, and acombination engine 635. Details of how the lexical generator 810connection outputs data between the engines is detailed in the followingfigures. At a high-level, the combination engine 635 combines vectorsfrom the word engine 610, the part-of-speech engine 615, the gazetteerengine 625, and the character engine 620 to generate a combined vectorwhich is input into the classification engine 640. The multimodalmessage 805 is further input into a visual word engine 630, which isconfigured to generate a word vector from the image and the caption ofthe multimodal message 805, as discussed in further detail below withreference to FIG. 12 . The classification engine 640 receives the inputdata from the visual word engine 630 and the lexical generator 810 andrecurrently generates (e.g. using a bidirectional LSTM neural network)tags indicating whether one or more of the words in the caption of themultimodal message is a named entity. For example, as illustrated, theclassification engine 640 can generate output data 815. For example, ifthe multimodal message included the caption “Florence and the machinesurprise patient with private concert in Austin!”, the output data 815may indicate that the term “Florence” is the beginning of a name and theterm machine is the end of a name of an entity using a type of tag ormetadata (as indicated by the bold font). Further the output data 815may also indicate that the term Austin is likely the city of Austin,Texas and not a name for example using it further tag or metadata (asindicated by the underlined font).

FIG. 9 shows an example architecture 900 of the gazetteer engine 625,according to some example embodiments. In FIG. 9 , the caption in themultimodal message is “Cristiano Rinaldo scores 3 in Portugal win”. Thegazetteer engine 625 can generate a matrix 905 which tags each word ofthe caption as corresponding to a gazetteer of a plurality of gazetteersused to train the gazetteer engine 625. In particular, for example, theterm Cristiano is labeled as a beginning tag of a gazetteer and Rinaldois labeled as the ending tag of a gazetteer of a same entity. Portugalis further labeled as belonging to a location-based gazetteer. Thegazetteer engine 625 consults a lookup table 910, which stores gazetteervectors 915 for each of the possible gazetteers and associates each wordin the caption with the is proper gazetteer vector if that term in thecaption is to receive a gazetteer vector (e.g. the term in may be a nullvector).

FIG. 10 shows an architecture 1000 for the lexical generator 810,according to some example embodiments. As illustrated, each character ofthe caption can be input into the character engine 620 whichincorporates a bidirectional LSTM. The combination engine 635concatenates a character embedding in a forward time direction, acharacter embedding in a backward time direction, a part-of-speechrepresentation for that word, a word embedding for that word, agazetteer representation of vector for that word, to generate thecombined vector 1005 which is input into the classification engine 640.The combined vector 1005 describes a single word of the caption, e.g.,“Florence” from “Florence and the Machine”.

FIG. 11 shows an example architecture 1100 of the classification engine640, according to some example embodiments. As illustrated, wordrepresentations may be input into a bidirectional LSTM and a conditionalrandom field (CRF), where each word in the caption is recursivelylabeled as belonging to a named entity. The word representations inputinto architecture 1100 may be the combined vectors (e.g. combined vector1005 generated from the lexical generator) and may also include a wordfrom the visual word engine 630.

FIG. 12 shows an example visual word engine 630 neural networkarchitecture 1200, according to some example embodiments. The visualword engine 630 comprises three regions including a sequence labellingmodel (comprising bidirectional LSTM layer 1221 and CRF layer 1222), avisual attention model 1218, and visual modulation gate 1220 layer. At ahigh-level, given a pair of caption 1204 and image 1202 as input, theVisual Attention Model 1218 extracts regional visual features from theimage and computes the weighted sum of the regional visual features asthe visual context vector 1224, based on their relatedness with thesentence. The BLSTM-CRF sequence labeling model predicts the label foreach word in the caption 1204 based on both the visual context vector1224 and the textual information of the words. The modulation gate 1220layer controls the combination of the visual context vector 1224 and theword representations for each word before the CRF layer 1222.

Sequence Labeling Model

The Sequence Labeling Model implements name tagging as a sequencelabeling problem. Given a sequence of words: S={s₁, s₂, . . . , s_(n)},the Sequence Labeling Model aims to predict a sequence of labels: L={l₁,l₂, . . . , l_(n)}, where l_(i)∈L and L is a pre-defined label set. TheSequence Labeling Model comprises bidirectional LSTM layer 1221, whichare variants of Recurrent Neural Networks (RNNs) designed to capturelong-range dependencies of input. The equations of a LSTM cell are asfollows:

i _(t)=σ(W _(xi) x _(t) +W _(hi) h _(t)−1+b _(i))

f _(t)=σ(Wxf xt+Whf ht−1+b _(f))

c _(t)=tan h(W _(xc) x _(t) +W _(hc) h _(t)−1+b _(c))

c _(t) =f _(t) ⊙c _(t)−1+i _(t) ⊙c _(t)

o _(t)=σ(W _(xo) X _(t) +W _(ho) h _(t)−1+b _(o))

h _(t) =o _(t)⊙ tan h(c _(t))

where x_(t), c_(t) and h_(t) are the input, memory and hid-den state attime t respectively. W_(xi), W_(hi), W_(xf), W_(hf), Wxc, W_(hc),W_(xo), and W_(ho) are weight matrices. ⊙ is the element-wise productfunction and σ is the element-wise sigmoid function. Sequence LabelingModel implements a Bidirectional LSTM because the name tagging taskbenefits from both of the past (left) and the future (right) contexts.In particular, the right and left context representations areconcatenated for each word: h_(t)=[h_(right), h_(left)].

The Sequence Labeling Model generates the character-level representation(“CHAR. EMB.”) for each word using another BLSTM, which is included inarchitecture 1200 but omitted for clarity. The BLSTM receives characterembeddings as input and generates representations combining implicitprefix, suffix and spelling information. The final word representationxi is the concatenation of word embedding el and character-levelrepresentation ci.

c _(i)=BLSTM_(char)(s _(i))s _(i) ∈S

x _(i) =[e _(i) ,c _(i)]

Further, the Sequence Labeling Model comprises a CRF layer 1222 toconsider constraint of the labels in the neighborhood. For example, anI-LOC must follow B-LOC, where LOC refers to the label being a locationtype and be is “Beginning” and “I” is intermediate”. Thus, asillustrated in FIG. 12 , Florence is a B-PER (where PER is for Persontype), which is followed by three non-beginning type I-PER labels.

Visual Attention Model

The image 1202 is input into a convolutional neural network (e.g.,Residual Network (ResNet) to generate visual vector 1212 comprisingvisual features for regional areas as well as for the entire image 1202:

V _(g)=ResNet_(g)(I)

V _(r)=ResNet_(r)(I)

where the global visual vector Vg, which represents the entire image1202, is the output before the last fully connected layer. The lastfully connected layer outputs the probabilities over 1000 classes,according to some example embodiments. The dimension of Vg is 1,024.V_(r) are the visual representations for regional areas and they areextracted from the last convolutional layer of the CNN 1210 (e.g., lastlayer of ResNet). The dimension of V_(r) is 1,024×7×7, where 7×7 is thenumber of regions in the image and 1,024 is dimension of the featurevector. Thus, each feature vector of V_(r) corresponds to a 32×32 pixelregion of the rescaled input image.

The global visual vector is a reasonable representation of the wholeinput image 1202 but is improvable. In some cases, only parts of animage are related to the associated caption. The visual attentionnetwork is configured to address further improve the visualrepresentation by focusing on regions of the input image that are morelikely related or useful in context of the corresponding caption.

In some example embodiments, the attention network 1216 maps a query anda set of key-value pairs to an output 1214. The output 1214 is aweighted sum of the values and the assigned weight for each value iscomputed by a function of the query and corresponding key. In someexample embodiments, the caption 1204 is encoded into a query 1208(e.g., encoded text) via a LSTM 1206. In particular, the LSTM 1206encodes the caption 1204 into encoded text, query 1208. The inputs intothe LSTM 1206 are the concatenations of word embeddings andcharacter-level word representations. Different from the LSTM model usedfor Sequence Labeling Model (e.g., “BKD. LSTM”, “FRW. LSTM”), the LSTM1206 aims to get the semantic information of the caption, and further itthe LSTM 1206 is unidirectional:

Q=LSTMquery(S)

The attention network 1216 receives the query and the regional visualrepresentations V_(r) as keys and values.

Attention Implementation: Based on experimental results, dot productimplementations result in more concentrated attentions and linearprojection after summation results in more dispersed attentions. In thecontext of name tagging, linear projection after summation isimplemented because it is beneficial for the model to utilize as manyrelated visual features as possible, and concentrated attentions maymake the model bias. For implementation, the text query vector Q andregional visual features V_(r) are projected into the same dimensions:

P _(t)=tan h(W _(t) Q)

P _(v)=tan h(W _(v) V _(r))

The projected query vector is then summed with each projected regionalvisual vector respectively:

A=P _(t) ⊕P _(v)

The weights of the regional visual vectors are:

E=softmax(W _(a) A+b _(a))

where Wa is weights matrix. The weighted sum of the regional visualfeatures is:

ν_(c)=Σα_(i)ν_(i) +i∈E,ν _(i) ∈V _(r)

The architecture uses v_(c) as the visual context vector 1224 toinitialize the BLSTM Sequence Labeling Model discussed above.

Visual Modulation Gate

In some example embodiments, the visual context vector 1224 is usedinitialize the LSTM cell, as indicated by the visual context vector 1224being input into the first cell of “FRW. LSTM”. In some exampleembodiment, the visual context vector 1224 is not used to initialize theLSTL but incorporated at the word level. Generally, visual featurescontribute differently when they are used to infer the tags of differentwords. For example, the architecture 1200 can easily find matched visualpatterns from associated images for verbs such as ‘sing’, ‘run’, and‘play’; and words/phrases such as names of basketball players, artists,and buildings are often well-aligned with objects in images. However, itis difficult to align function words such as ‘the’, ‘of’ and ‘well’ withvisual features. Fortunately, most of the challenging cases in nametagging involve nouns and verbs, the disambiguation of which can benefitmore from visual features. As such, according to some exampleembodiments, the visual context vector 1224 is incorporated at wordlevel individually, as indicated by the dotted lines from visual contextvector 1224 to respective instances of gates in the modulation gate 1220layer.

The modulation gate 1220 dynamically controls the combination of visualfeatures and word representation generated by BLSTM at word-level,before feeding them into the CRF layer 1222 for tag prediction. Theequations for the implementation of modulation gate 1220 are as follows:

β_(v)=σ(W _(v) h _(i) +U _(v) v _(c) +b _(v))

β_(w)=σ(V _(w) h _(i) +U _(w) v _(c) +b _(w))

m=tan h(W _(m) h _(i) +U _(m) v _(c) +b _(m))

w _(m)=β_(w) ·h _(i)+β_(v) ·m

where h_(i) is the word representation generated by BLSTM, v_(c) is thecomputed visual context vector, W_(v), V_(w), W_(m), U_(v), U_(w) andU_(m) are weight matrices, σ is the element-wise sigmoid function, andw_(m) is the modulated word representations input into the CRF layer1222.

FIG. 13A shows an image 1202 of the multimodal message, according tosome example embodiments. The image 1202 depicts a patient 1305 on ahospital bed and a partially out-of-frame guitar player 1310. Thecorresponding caption 1204 (not depicted in FIG. 13A) is the sentence“Florence and the machine surprise teen with private concert”.Conventionally, the caption 1204 can be difficult to parse because“Florence” could be the name of a person in the image, or refer tosomething else, such as one term of a band's full name.

FIG. 13B shows an example output of attention network 1216, according tosome example embodiments. As illustrated, the attention network 1216 hasrelevant portions 1315 of the image 1202, thereby minimizing areas notrelated to the context of the caption. In particular, by using theregional image data (e.g., V_(r), a portion of which depicts a guitar)and the caption 1204, the entity “Florence and the Machine” isidentified as a named entity being discussed in the multimodal message.

FIG. 14A shows an example multimodal message 1400, according to someexample embodiments. As illustrated, the multimodal message 1400includes an image of a person on stage 1405 in front of an audience1410, and a caption 1415 (“JUUUUUUSTIN!!!!<3<3<3” comprising anintentional misspelling of “Justin” and three pictorial heart symbols:“<3”) that has been input by a user of the client device 102. Multimodalmessage 1400 exam is an example of the multimodal message received orotherwise generated at operation 705 of FIG. 7A. FIG. 14B shows exampleoverlay content 1420 that has been identified by performing the method700 of FIG. 7A, according to some example embodiments. In particular,for example, the visual named entity recognition system 210 hasdetermined that the caption includes a reference to a named entity, thatof the famous singer Justin Bieber. Accordingly, the overlay content1420 that has been pre-associated with that named entity is retrievedfrom database 126 and overlaid on the multimodal message 1400 to bestored or otherwise published to a network site as an ephemeral message.

FIG. 15 is a block diagram illustrating an example software architecture1506, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 15 is a non-limiting example of asoftware architecture, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 1506 may execute on hardwaresuch as a machine 1150 of FIG. 11 that includes, among other things,processors, memory, and input/output (I/O) components. A representativehardware layer 1552 is illustrated and can represent, for example, themachine 1150 of FIG. 11 . The representative hardware layer 1552includes a processing unit 1554 having associated executableinstructions 1504. The executable instructions 1504 represent theexecutable instructions of the software architecture 1506, includingimplementation of the methods, components, and so forth describedherein. The hardware layer 1552 also includes a memory/storage 1556,which also has the executable instructions 1504. The hardware layer 1552may also comprise other hardware 1558.

In the example architecture of FIG. 15 , the software architecture 1506may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 1506may include layers such as an operating system 1502, libraries 1520,frameworks/middleware 1518, applications 1516, and a presentation layer1511. Operationally, the applications 1516 and/or other componentswithin the layers may invoke API calls 1508 through the software stackand receive a response in the form of messages 1512. The layersillustrated are representative in nature and not all softwarearchitectures have all layers. For example, some mobile orspecial-purpose operating systems may not provide aframeworks/middleware 1518, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 1502 may manage hardware resources and providecommon services. The operating system 1502 may include, for example, akernel 1522, services 1524, and drivers 1526. The kernel 1522 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 1522 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1524 may provideother common services for the other software layers. The drivers 1526are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1526 include display drivers, cameradrivers, Bluetooth® drivers, flash memory drivers, serial communicationdrivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers,audio drivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 1520 provide a common infrastructure that is used by theapplications 1516 and/or other components and/or layers. The libraries1520 provide functionality that allows other software components toperform tasks in an easier fashion than by interfacing directly with theunderlying operating system 1502 functionality (e.g., kernel 1522,services 1524, and/or drivers 1526). The libraries 1520 may includesystem libraries 1544 (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematical functions, and the like. In addition, thelibraries 1520 may include API libraries 1546 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, or PNG),graphics libraries (e.g., an OpenGL framework that may be used to render2D and 3D graphical content on a display), database libraries (e.g.,SQLite that may provide various relational database functions), weblibraries (e.g., WebKit that may provide web browsing functionality),and the like. The libraries 1520 may also include a wide variety ofother libraries 1548 to provide many other APIs to the applications 1516and other software components/modules.

The frameworks/middleware 1518 provide a higher-level commoninfrastructure that may be used by the applications 1516 and/or othersoftware components/modules. For example, the frameworks/middleware 1518may provide various graphic user interface (GUI) functions, high-levelresource management, high-level location services, and so forth. Theframeworks/middleware 1518 may provide a broad spectrum of other APIsthat may be utilized by the applications 1516 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system 1502 or platform.

The applications 1516 include built-in applications 1538 and/orthird-party applications 1540. Examples of representative built-inapplications 1538 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. The third-party applications 1540 may includean application developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 1540 may invoke the API calls 1508 provided bythe mobile operating system (such as the operating system 1502) tofacilitate functionality described herein.

The applications 1516 may use built-in operating system functions (e.g.,kernel 1522, services 1524, and/or drivers 1526), libraries 1520, andframeworks/middleware 1518 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such asthe presentation layer 1511. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

FIG. 16 is a block diagram illustrating components of a machine 1600,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 16 shows a diagrammatic representation of the machine1600 in the example form of a computer system, within which instructions1616 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1600 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 1616 may be used to implement modules or componentsdescribed herein. The instructions 1616 transform the general,non-programmed machine 1600 into a particular machine 1600 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1600 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1600 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1600 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smartphone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1616, sequentially or otherwise, that specify actions to betaken by the machine 1600. Further, while only a single machine 1600 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 1616 to perform any one or more of the methodologiesdiscussed herein.

The machine 1600 may include processors 1610, memory/storage 1630, andI/O components 1650, which may be configured to communicate with eachother such as via a bus 1602. The memory/storage 1630 may include amemory 1632, such as a main memory, or other memory storage, and astorage unit 1636, both accessible to the processors 1610 such as viathe bus 1602. The storage unit 1636 and memory 1632 store theinstructions 1616 embodying any one or more of the methodologies orfunctions described herein. The instructions 1616 may also reside,completely or partially, within the memory 1632, within the storage unit1636, within at least one of the processors 1610 (e.g., within theprocessor cache memory accessible to processor units 1612 or 1614), orany suitable combination thereof, during execution thereof by themachine 1600. Accordingly, the memory 1632, the storage unit 1636, andthe memory of the processors 1610 are examples of machine-readablemedia.

The I/O components 1650 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1650 that are included in a particular machine 1600 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 1650 may include many other components that are not shown inFIG. 16 . The I/O components 1650 are grouped according to functionalitymerely for simplifying the following discussion and the grouping is inno way limiting. In various example embodiments, the I/O components 1650may include output components 1652 and input components 1654. The outputcomponents 1652 may include visual components (e.g., a display such as aplasma display panel (PDP), a light-emitting diode (LED) display, aliquid-crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1654 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1650 may includebiometric components 1656, motion components 1658, environmentcomponents 1660, or position components 1662 among a wide array of othercomponents. For example, the biometric components 1656 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram-basedidentification), and the like. The motion components 1658 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environment components 1660 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gassensors to detect concentrations of hazardous gases for safety or tomeasure pollutants in the atmosphere), or other components that mayprovide indications, measurements, or signals corresponding to asurrounding physical environment. The position components 1662 mayinclude location sensor components (e.g., a GPS receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1650 may include communication components 1664operable to couple the machine 1600 to a network 1680 or devices 1670via a coupling 1682 and a coupling 1672, respectively. For example, thecommunication components 1664 may include a network interface componentor other suitable device to interface with the network 1680. In furtherexamples, the communication components 1664 may include wiredcommunication components, wireless communication components, cellularcommunication components, near field communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 1670 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 1664 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1664 may include radio frequency identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional barcodes such as Universal Product Code (UPC) barcode,multi-dimensional barcodes such as Quick Response (QR) code, Aztec code,Data Matrix, Dataglyph, MaxiCode, PDF418, Ultra Code, UCC RSS-2Dbarcode, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1664, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

Glossary

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions 1616 forexecution by the machine 1600, and includes digital or analogcommunications signals or other intangible media to facilitatecommunication of such instructions 1616. Instructions 1616 may betransmitted or received over the network 1680 using a transmissionmedium via a network interface device and using any one of a number ofwell-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine 1600 thatinterfaces to a communications network 1680 to obtain resources from oneor more server systems or other client devices 102. A client device 102may be, but is not limited to, a mobile phone, desktop computer, laptop,PDA, smartphone, tablet, ultrabook, netbook, multi-processor system,microprocessor-based or programmable consumer electronics system, gameconsole, set-top box, or any other communication device that a user mayuse to access a network 1680.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network 1680 that may be an ad hoc network, an intranet, anextranet, a virtual private network (VPN), a local area network (LAN), awireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network 1680 may include a wireless or cellular networkand the coupling may be a Code Division Multiple Access (CDMA)connection, a Global System for Mobile communications (GSM) connection,or another type of cellular or wireless coupling. In this example, thecoupling may implement any of a variety of types of data transfertechnology, such as Single Carrier Radio Transmission Technology(1×RTT), Evolution-Data Optimized (EVDO) technology, General PacketRadio Service (GPRS) technology, Enhanced Data rates for GSM Evolution(EDGE) technology, third Generation Partnership Project (3GPP) including3G, fourth generation wireless (4G) networks, Universal MobileTelecommunications System (UMTS), High-Speed Packet Access (HSPA),Worldwide Interoperability for Microwave Access (WiMAX), Long-TermEvolution (LTE) standard, others defined by various standard-settingorganizations, other long-range protocols, or other data transfertechnology.

“EMPHEMERAL MESSAGE” in this context refers to a message 400 that isaccessible for a time-limited duration. An ephemeral message 502 may bea text, an image, a video, and the like. The access time for theephemeral message 502 may be set by the message sender. Alternatively,the access time may be a default setting or a setting specified by therecipient. Regardless of the setting technique, the message 400 istransitory.

“MACHINE-READABLE MEDIUM” in this context refers to a component, adevice, or other tangible media able to store instructions 1616 and datatemporarily or permanently and may include, but is not limited to,random-access memory (RAM), read-only memory (ROM), buffer memory, flashmemory, optical media, magnetic media, cache memory, other types ofstorage (e.g., erasable programmable read-only memory (EPROM)), and/orany suitable combination thereof. The term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions 1616. The term “machine-readable medium”shall also be taken to include any medium, or combination of multiplemedia, that is capable of storing instructions 1616 (e.g., code) forexecution by a machine 1600, such that the instructions 1616, whenexecuted by one or more processors 1610 of the machine 1600, cause themachine 1600 to perform any one or more of the methodologies describedherein. Accordingly, a “machine-readable medium” refers to a singlestorage apparatus or device, as well as “cloud-based” storage systems orstorage networks that include multiple storage apparatus or devices. Theterm “machine-readable medium” excludes signals per se.

“COMPONENT” in this context refers to a device, a physical entity, orlogic having boundaries defined by function or subroutine calls, branchpoints, APIs, or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor 1612 ora group of processors 1610) may be configured by software (e.g., anapplication or application portion) as a hardware component thatoperates to perform certain operations as described herein. A hardwarecomponent may also be implemented mechanically, electronically, or anysuitable combination thereof. For example, a hardware component mayinclude dedicated circuitry or logic that is permanently configured toperform certain operations. A hardware component may be aspecial-purpose processor, such as a field-programmable gate array(FPGA) or an application-specific integrated circuit (ASIC). A hardwarecomponent may also include programmable logic or circuitry that istemporarily configured by software to perform certain operations. Forexample, a hardware component may include software executed by ageneral-purpose processor or other programmable processor. Onceconfigured by such software, hardware components become specificmachines (or specific components of a machine 1600) uniquely tailored toperform the configured functions and are no longer general-purposeprocessors 1610. It will be appreciated that the decision to implement ahardware component mechanically, in dedicated and permanently configuredcircuitry, or in temporarily configured circuitry (e.g., configured bysoftware) may be driven by cost and time considerations. Accordingly,the phrase “hardware component” (or “hardware-implemented component”)should be understood to encompass a tangible entity, be that an entitythat is physically constructed, permanently configured (e.g.,hardwired), or temporarily configured (e.g., programmed) to operate in acertain manner or to perform certain operations described herein.

Considering embodiments in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processor 1612configured by software to become a special-purpose processor, thegeneral-purpose processor 1612 may be configured as respectivelydifferent special-purpose processors (e.g., comprising differenthardware components) at different times. Software accordingly configuresa particular processor 1612 or processors 1610, for example, toconstitute a particular hardware component at one instance of time andto constitute a different hardware component at a different instance oftime.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inembodiments in which multiple hardware components are configured orinstantiated at different times, communications between or among suchhardware components may be achieved, for example, through the storageand retrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors 1610 that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors 1610 may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors1610. Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor 1612 or processors1610 being an example of hardware. For example, at least some of theoperations of a method may be performed by one or more processors 1610or processor-implemented components. Moreover, the one or moreprocessors 1610 may also operate to support performance of the relevantoperations in a “cloud computing” environment or as a “software as aservice” (SaaS). For example, at least some of the operations may beperformed by a group of computers (as examples of machines 1600including processors 1610), with these operations being accessible via anetwork 1680 (e.g., the Internet) and via one or more appropriateinterfaces (e.g., an API). The performance of certain of the operationsmay be distributed among the processors 1610, not only residing within asingle machine 1600, but deployed across a number of machines 1600. Insome example embodiments, the processors 1610 or processor-implementedcomponents may be located in a single geographic location (e.g., withina home environment, an office environment, or a server farm). In otherexample embodiments, the processors 1610 or processor-implementedcomponents may be distributed across a number of geographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor1612) that manipulates data values according to control signals (e.g.,“commands,” “op codes,” “machine code,” etc.) and which producescorresponding output signals that are applied to operate a machine 1600.A processor may, for example, be a central processing unit (CPU), areduced instruction set computing (RISC) processor, a complexinstruction set computing (CISC) processor, a graphics processing unit(GPU), a digital signal processor (DSP), an ASIC, a radio-frequencyintegrated circuit (RFIC), or any combination thereof. A processor 1610may further be a multi-core processor 1610 having two or moreindependent processors 1612, 1614 (sometimes referred to as “cores”)that may execute instructions 1616 contemporaneously.

“TIMESTAMP” in this context refers to a sequence of characters orencoded information identifying when a certain event occurred, forexample giving date and time of day, sometimes accurate to a smallfraction of a second.

What is claimed is:
 1. A method comprising: identifying, using one ormore processors of a machine, a multimodal message that includes animage and a caption comprising words; generating, using an attentionneural network, a visual context vector from the caption and the image,the visual context vector emphasizing portions of the caption based onobjects depicted in the image; initializing an entity recognition neuralnetwork using the visual context vector as an initial data input item;generating, using the entity recognition neural network, an indicationthat one or more words of the caption correspond to a named entity; andstoring the one or more words as the named entity of the multimodalmessage.
 2. The method of claim 1, further comprising: selecting one ormore items of content using the named entity.
 3. The method of claim 2,further comprising: generating a modified multimodal message comprisingthe image, the caption, and the one or more items of content.
 4. Themethod of claim 1, wherein the attention neural network comprises aconvolutional neural network, and the method further comprises:generating, using the convolutional neural network, one or more imagevectors from the image, wherein the attention neural network generatesthe visual context vector using the one or more image vectors and thecaption.
 5. The method of claim 4, wherein the one or more image vectorscomprise a global vector corresponding to the image and regional vectorscorresponding to regions of the image.
 6. The method of claim 1, whereinthe words of the caption comprise at least one or more of: individualcharacters, symbols, a sequence of characters.
 7. The method of claim 1,wherein the entity recognition neural network comprises a bi-directionalneural network and a conditional random field layer.
 8. The method ofclaim 1, further comprising: generating encoded text from the captionusing a recurrent neural network, wherein the attention neural networkgenerates the visual context vector at least in part from the encodedtext.
 9. The method of claim 1, further comprising: integrating, using amodulation layer, the visual context vector into the entity recognitionneural network for each word in the caption.
 10. The method of claim 1,wherein the entity recognition neural network and the attention neuralnetwork are trained end-to-end.
 11. A system comprising: one or moreprocessors of a machine; and a memory storing instructions that, whenexecuted by the one or more processors, cause the machine to performoperations comprising: identifying, using the one or more processors ofthe machine, a multimodal message that includes an image and a captioncomprising words; generating, using an attention neural network, avisual context vector from the caption and the image, the visual contextvector emphasizing portions of the caption based on objects depicted inthe image; initializing an entity recognition neural network using thevisual context vector as an initial data input item; generating, usingthe entity recognition neural network, an indication that one or morewords of the caption correspond to a named entity; and storing the oneor more words as the named entity of the multimodal message.
 12. Thesystem of claim 11, wherein the operations further comprise: selectingone or more items of content using the named entity.
 13. The system ofclaim 12, wherein the operations further comprise: generating a modifiedmultimodal message comprising the image, the caption, and the one ormore items of content.
 14. The system of claim 11, wherein the attentionneural network comprises a convolutional neural network, and theoperations further comprise: generating, using the convolutional neuralnetwork, one or more image vectors from the image, wherein the attentionneural network generates the visual context vector using the one or moreimage vectors and the caption.
 15. The system of claim 14, wherein theone or more image vectors comprise a global vector corresponding to theimage and regional vectors corresponding to regions of the image. 16.The system of claim 11, wherein the words of the caption comprise atleast one or more of: individual characters, symbols, a sequence ofcharacters.
 17. The system of claim 11, wherein the entity recognitionneural network comprises a bi-directional neural network and aconditional random field layer.
 18. The system of claim 11, wherein theoperations further comprise: generating encoded text from the captionusing a recurrent neural network, wherein the attention neural networkgenerates the visual context vector at least in part from the encodedtext.
 19. The system of claim 11, wherein the operations furthercomprise: integrating, using a modulation layer, the visual contextvector into the entity recognition neural network for each word in thecaption.
 20. A machine-readable storage device embodying instructionsthat, when executed by a machine, cause the machine to performoperations comprising: identifying, using one or more processors of themachine, a multimodal message that includes an image and a captioncomprising words; generating, using an attention neural network, avisual context vector from the caption and the image, the visual contextvector emphasizing portions of the caption based on objects depicted inthe image; initializing an entity recognition neural network using thevisual context vector as an initial data input item; generating, usingthe entity recognition neural network, an indication that one or morewords of the caption correspond to a named entity; and storing the oneor more words as the named entity of the multimodal message.